Cuffless Blood Pressure Estimation from Phonocardiogram Signals Using Deep Learning with Adaptive Feature Recalibration

Blood pressure (BP) monitoring is essential for cardiovascular health management, yet traditional cuff-based methods face limitations including patient discomfort and inapplicability for certain populations. This study presents a deep learning framework for cuffless BP estimation using phonocardiogram (PCG) signals. The proposed model integrates convolutional neural networks (CNNs) with Squeeze-and-Excitation (SE) blocks and demographic information to enhance prediction accuracy. Mel-Frequency Cepstral Coefficients (MFCCs), along with their delta and delta–delta coefficients, were employed to capture comprehensive acoustic characteristics of heart sounds. The results demonstrated that the proposed model achieved high predictive accuracy and strong consistency with reference BP measurements. Component analysis confirmed that the inclusion of SE blocks provided substantial performance gains, while demographic information further improved prediction stability. Clinical validation also verified that the model maintained close agreement with true BP values across the tested population, showing significant improvement over the baseline CNN implementation. These findings suggest potential for accessible, non-invasive BP monitoring systems suitable for continuous health tracking.